This first project will introduce you to creating RMarkdown files and doing basic data manipulation inside R and RStudio.

Question 1

In an r chunk, perform two operations: first add the numbers 9 and 23, then divide the number 42 by the sum of 83 and 101. Round your second answer to three decimal places.

# Adding 9 + 23
9+23
## [1] 32
# Dividing 42 by (83 + 101)
43/(83+101)
## [1] 0.2336957

Answer: The sum of the numbers 9 and 23 is 32.

Answer: The quotient of 42 and (83 + 101) rounded to 3 decimal places is 0.234.

Question 2

In an r chunk, store the dataframe mtcars into an object called mtcars and use the head() function to look at the first 6 rows of that object.

# Store the mtcars dataframe into an object called mtcars
mtcars <- mtcars

# View the first 6 rows of the mtcars dataframe
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Question 3

Use the ?mtcars command in the RStudio Console to pull up the help documentation to find out what the qsec variable describes in the mtcars dataset.

Answer: The qsec variable in the mtcars dataset describes 1/4 of the car’s mile time.

Question 4

To load a package into R requires the use of the install.packages() and library() functions. Type: install.packages("openintro") into the RStudio Console to install that package on to your computer. The install.packages() function requires a character input, so " " are needed around the input.

The library() function takes in an object name, so do not use " " around the object name.

For this question, simply use an r chunk to load the library openintro which contains some datasets we will use going in subsequent problems.

# Load openintro library
library(openintro)

Question 5

With the openintro library loaded, you can now access some datasets from it. In an r chunk, find the dimensions of the email dataset by using the dim() function on it.

# Dimensions of the email dataframe
dim(email)
## [1] 3921   21

Answer: The email dataframe contains 3,921 rows and 21 columns.

Question 6

The $ sign is used to specify a certain variable of a dataframe. For example, the following will give me the sum of the cylinders variable in the mtcars dataframe.

# Sum of the cyl variable in mtcars dataframe
sum(mtcars$cyl)
## [1] 198

In the email dataframe, what is the sum of the values in the line_breaks variable?

# Sum of the line_breaks variable in the email dataframe
sum(email$line_breaks)
## [1] 904412

Answer: The sum of line breaks in the email dataframe is 904,412.

Question 7

In the email dataset, the first variable called spam has a value of 0 if the email was not spam and a value of 1 if the email was spam. You can use the table() function on the spam variable to see how many emails fell into the spam and non-spam categories.

In an r chunk, calculate what percent of the emails in this dataframe were considered spam? (Then state your answer rounded to nearest tenth of a percent)

# Table of spam/not-spam emails
table(email$spam)
## 
##    0    1 
## 3554  367

# Percent of emails that were spam
#Calculate total number of emails from 3554 non-spam and 367 spam emails
3554+367
## [1] 3921
#Divide the number of spam emails by the total number of emails
367/3921
## [1] 0.09359857
#multiple the answer, 0.9064014, by 100 to form a percentage
.09359857*100
## [1] 9.359857

Answer: 9.4% of the emails were spam emails.

Question 8

We can use the subset() function to get subsets of a dataframe based on a given characteristic.

For example, the code below will store a new dataframe that only contains the observations in which the word “dollar” or a $ sign was found in the email.

# Storing emails with "dollar" in them to an object called money
money <- subset(email, email$dollar > 0)

Find the percent of emails in the money subset that are spam.

# Enter your own comments here explaining your code
#Use the table function to see how many emails in the money subset are spam and how many are not
table(money$spam)
## 
##   0   1 
## 668  78
#There are 78 spam emails, and 668 non-spam emails in the subset. Add these to get the total number of emails.
668+78
## [1] 746
#There are a total of 746 emails in the money subset. Divide the number of spam emails by the total nubmer of emails in the money subset, and multiply by 100 to form a percentage.
(78/746)*100
## [1] 10.45576

Answer: 10.5% percent of the emails in the money subset are spam.

Comparing your answers from #7 and #8, do dollar signs or the word “dollar” seem to be a key characteristic of emails that are spam? Explain why or why not.

Answer: Comparing the answers from #7 and #8, dollar signs and the word “dollar” do not appear to be a key characteristic of emails that are spam. This is because the two percentages representing spam messages, 9.4% of all emails and 10.5% of emails in the money subset, were so close to each other. At first glance it may imply that there is a slightly higher chance that money related emails are spam, but this may be due to natural variation. Overall, because the two values are so close, using dollar signs or the word “dollar” does not appear to be a key characteristic of spam emails.

Question 9

Type View(airquality) into your RStudio console (not in an r chunk) and you will see a tab open up with the dataset in it for viewing. The dataframe gives air quality measurements in NYC over a 153 day period in the year 1973. In the chunk below, I have stored the airquality dataframe into an object called airquality and returned the names of the variables.

# Store airquality dataframe in object called airquality
airquality <- airquality

# Find the names of the variables in the dataframe
names(airquality)
## [1] "Ozone"   "Solar.R" "Wind"    "Temp"    "Month"   "Day"

Answer: The variables are Ozone, Solar.R, Wind, Temp, Month, and Day.

Type ?airquality into the RStudio console to find out what the variable names in the dataframe represent.

This chunk below tries to find the sum of the solar radiation in langleys (a unit of solar radiation), but there is a problem, it returns NA.

# Find sum of Solar.R variable
sum(airquality$Solar.R)
## [1] NA

If you type View(airquality) into your RStudio console again, you can see that there are some values in the Solar.R variable, but also some NA values, which the sum function cannot handle.

Type ?sum into your RStudio console and look at the help documentation for that function. You’ll see in the Usage section sum(..., na.rm = FALSE). The second argument, na.rm, stands for “remove NAs” and this function defaults to FALSE, or no, do not remove NAs. If we want the sum function to ignore the NAs in a vector, we just need to set na.rm = TRUE as a second argument inside the function.

Find the sum of the solar radiation (the Solar.R variable) by ignoring NAs.

# Sum of Solar.R variable ignoring the NAs
sum(airquality$Solar.R, na.rm=TRUE)
## [1] 27146

The sum of the Solar.R variable is 27,146.

Question 10

Often we will need to load our own data into R for analysis. A common file type that data is stored in is called a “.csv” file or “comma separated values”. The common way to load a .csv in R is with the read.csv() function and storing it to an object.

Example:
new_data <- read.csv("newdata.csv")

For this question:

# Set working directory to the folder where file exists
setwd("~/Desktop")
# Read new dataset and store it as project_data
project_data <- read.csv("~/Desktop/mens_health.csv")

# Find the sum of any variable
sum(project_data$WT)
## [1] 6902

Answer: The sum of the WT variable is 6,902.